machine learning: inteligencia artificial no es sólo un tema de ciencia ficción by raul garreta
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Machine Learning: Inteligencia Artificial no es sólo un tema de Ciencia Ficción Raul Garreta .NET Conf UY 2014 http://netconf.uyTRANSCRIPT
Machine Learning: Artificial Intelligence isn't just a Science Fiction topic
Raul Garreta - Tryolabs / MonkeyLearn
My Credentials● Computer Science Engineer from Udelar,
Msc in Machine Learning + NLP● Co-Founder, CTO & Product Manager at
Tryolabs.● Co-Founder at MonkeyLearn.● Professor in ML at InCo, Udelar.● Co-authored "Learning Scikit-learn:
Machine Learning in Python"
Contents● Brief intro to AI & Machine Learning (ML)● ML Applications● Cloud ML tools
What is AI?From a behavioral point of view, is an artificial agent that shows certain characteristics of intelligence like:● Reasoning● Knowledge representation● Learning● Planning● Perception
What is AI? Behavioral test = Turing Test
If I write an enough complex If-then-else structure, could it pass the test?
Random behavior?
Different fields within AIArtificial Intelligence
● General Artificial Intelligence● Expert Systems
○ Natural Language Processing○ Computer Vision○ Machine Learning○ ...
Machine LearningAlgorithms that allow computers to automatically learn to perform a task from data.
Can improve their performance over time, by adding more data.
Machine Learning DefinitionsArthur Samuel (1959): "Field of study that gives computers the ability to learn without being explicitly programmed"
Tom Mitchell (1997): "A computer program is said to learn if its performance at a task T, as measured by a performance P, improves with experience E"
Machine Learning Algorithms
● Learn to associate a particular input (set of features) to a particular output (class, number or group of instances)
● That is the process of training a ML model.● And use the learned model to predict the
outcome on new instances
Inputs: InstancesUsually we have instances of data that represent objects: documents, images, users, etc.And can be represented by a set of features:● A document is represented by a set of words.● An image is represented by a set of pixels.● A user can be represented by the age, level of
education, gender, interests, etc.
Machine Learning ProblemsClassification: assign a label (class) to a set of items.
Regression: assign a number (evaluation) to a set of items
Clustering: group items into clusters according to a similarity measure
Type of Machine Learning Algorithms
Decision TreesLinear Models
Type of Machine Learning Algorithms
Probabilistic / Statistical Models
Neural Networks / Deep Learning
Important Concepts in MLBesides the Machine Learning…● Data gathering / importation● Data preprocessing● Feature extraction● Feature selection● Performance evaluation (testing)
ApplicationsNatural Language Processing
Text Mining Speech to Text
Applications: Computer Vision
Face Recognition OCR
ApplicationsData Mining / Predictive Analytics
Recommendation Engines Medicine
ApplicationsIntelligent Agents
Robotics Game Players
Why use Machine Learning?● Solve problems that manually would be extremely
difficult or impossible.● Make predictions.● Automatically process huge amounts of information and
sources: big data.● Intelligent apps => improve UX => improve conversion
rates => $$$● Great companies use it...
● Avoid to deploy and maintain the full stack.● Be cross platform.● Not all programming languages have ML
tools.● ML requires huge amounts of computer
power.● Just solve it: good, fast, easy.
Why use a Cloud Saas ML platform?
As with other problems (eg: payments, communications) is a trend to go SaaS.
Machine Learning Platforms
Machine Learning
Microsoft Azure ML● http://azure.microsoft.com/en-
us/services/machine-learning/● Launched preview version on June 2014.● Cloud based ML platform to build predictive
numerical applications.● Technologies used in Xbox and Bing.
Machine Learning
Microsoft Azure ML● Easy to scale, Azure infrastructure.● Users can build custom R modules.● GUI and APIs.● More oriented to Data Scientists.● Pricing: pay as you go.
Machine Learning
MonkeyLearn● http://monkeylearn.com/● Launched private alpha on April 2014● Cloud based, focused on Text Mining:
extract and classify information from text.
MonkeyLearn● Easy to use.● Pre-trained modules for different
applications.● GUI and APIs.● More oriented to developers.● Pricing: freemium, pay as you go.
Conclusions● Machine Learning can allow
us to make intelligent apps.● It's a trendy topic…● New ML platforms are
emerging, allowing any developer to incorporate ML technologies.